This document defines key vocabulary terms used in statistics, including population, sample, qualitative and quantitative data, different types of sampling methods like cluster and stratified sampling, ways to display data through histograms, and measures like mean, mode, standard deviation, and standard scores that are used to analyze and describe datasets. It also describes concepts like skewed distributions and the empirical rule.

1. Vocabulary Terms for Statistics
Population: Collection of all counts that are of interest
Sample: A subset or part of a population
Qualitive Data: Consists of atributes, labels, or nonnumerical enteries
Cluster Sample: A sample where the population is divided into groups and
several groups are radomly selected and all from those selected groups are
sampled
Stratified Sample: A sample where the population is divided into groups
and several are randomly sampled from each group
Histogram: Vertical bar chart that shows frequency on the y-axis
Mean: The average
Mode: The most frequent number appearing in a dataset
Right Skewed Distribution: Where the frequency distribution has a “tail”
that extends to the right with most of the data on the left side of the
distribution
Left Skewed Distribution: Where the frequency distribution has a “tail”
that extends to the left with most of the data on the right side of the
distribution
Standard Deviation: The square root of the variance
Emperical Rule: Describes the percentage of data that falls with 1, 2, or 3
standard deviations of the mean in a symmetrical, bell shaped distribution
Standard Score: Shows how far a particular datapoint is from the mean in
terms of the number of standard deviations